Visual Studio Code Python Interpreter

  

How To Install Visual Studio Code Python Extension. Open visual studio code, click View — Extensions menu item at the top menu bar. It will list all the installed and recommend extensions in the visual studio code left side EXTENSIONS panel.; Input the keyword python in the search box, and click the Install button for the Microsoft-created python extension to install it. Python is an interpreted language, and in order to run Python code and get Python IntelliSense, you must tell VS Code which interpreter to use. From within VS Code, select a Python 3 interpreter by opening the Command Palette ( ⇧⌘P (Windows, Linux Ctrl+Shift+P ) ), start typing the Python: Select Interpreter command to search, then select the command.

In this tutorial, you use Python 3 to create the simplest Python 'Hello World' application in Visual Studio Code. By using the Python extension, you make VS Code into a great lightweight Python IDE (which you may find a productive alternative to PyCharm).

This tutorial introduces you to VS Code as a Python environment, primarily how to edit, run, and debug code through the following tasks:

  • Write, run, and debug a Python 'Hello World' Application
  • Learn how to install packages by creating Python virtual environments
  • Write a simple Python script to plot figures within VS Code

This tutorial is not intended to teach you Python itself. Once you are familiar with the basics of VS Code, you can then follow any of the programming tutorials on python.org within the context of VS Code for an introduction to the language.

If you have any problems, feel free to file an issue for this tutorial in the VS Code documentation repository.

Prerequisites

To successfully complete this tutorial, you need to first setup your Python development environment. Specifically, this tutorial requires:

  • VS Code
  • VS Code Python extension
  • Python 3

Install Visual Studio Code and the Python Extension

  1. If you have not already done so, install VS Code.

  2. Next, install the Python extension for VS Code from the Visual Studio Marketplace. For additional details on installing extensions, see Extension Marketplace. The Python extension is named Python and it's published by Microsoft.

Install a Python interpreter

Along with the Python extension, you need to install a Python interpreter. Which interpreter you use is dependent on your specific needs, but some guidance is provided below.

Windows

Install Python from python.org. You can typically use the Download Python button that appears first on the page to download the latest version.

Note: If you don't have admin access, an additional option for installing Python on Windows is to use the Microsoft Store. The Microsoft Store provides installs of Python 3.7, Python 3.8, and Python 3.9. Be aware that you might have compatibility issues with some packages using this method.

For additional information about using Python on Windows, see Using Python on Windows at Python.org

macOS

The system install of Python on macOS is not supported. Instead, an installation through Homebrew is recommended. To install Python using Homebrew on macOS use brew install python3 at the Terminal prompt.

Note On macOS, make sure the location of your VS Code installation is included in your PATH environment variable. See these setup instructions for more information.

Linux

The built-in Python 3 installation on Linux works well, but to install other Python packages you must install pip with get-pip.py.

Other options

  • Data Science: If your primary purpose for using Python is Data Science, then you might consider a download from Anaconda. Anaconda provides not just a Python interpreter, but many useful libraries and tools for data science.

  • Windows Subsystem for Linux: If you are working on Windows and want a Linux environment for working with Python, the Windows Subsystem for Linux (WSL) is an option for you. If you choose this option, you'll also want to install the Remote - WSL extension. For more information about using WSL with VS Code, see VS Code Remote Development or try the Working in WSL tutorial, which will walk you through setting up WSL, installing Python, and creating a Hello World application running in WSL.

Verify the Python installation

To verify that you've installed Python successfully on your machine, run one of the following commands (depending on your operating system):

  • Linux/macOS: open a Terminal Window and type the following command:

  • Windows: open a command prompt and run the following command:

If the installation was successful, the output window should show the version of Python that you installed.

Note You can use the py -0 command in the VS Code integrated terminal to view the versions of python installed on your machine. The default interpreter is identified by an asterisk (*).

Start VS Code in a project (workspace) folder

Using a command prompt or terminal, create an empty folder called 'hello', navigate into it, and open VS Code (code) in that folder (.) by entering the following commands:

Note: If you're using an Anaconda distribution, be sure to use an Anaconda command prompt.

By starting VS Code in a folder, that folder becomes your 'workspace'. VS Code stores settings that are specific to that workspace in .vscode/settings.json, which are separate from user settings that are stored globally.

Alternately, you can run VS Code through the operating system UI, then use File > Open Folder to open the project folder.

Select a Python interpreter

Python is an interpreted language, and in order to run Python code and get Python IntelliSense, you must tell VS Code which interpreter to use.

From within VS Code, select a Python 3 interpreter by opening the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), start typing the Python: Select Interpreter command to search, then select the command. You can also use the Select Python Environment option on the Status Bar if available (it may already show a selected interpreter, too):

The command presents a list of available interpreters that VS Code can find automatically, including virtual environments. If you don't see the desired interpreter, see Configuring Python environments.

Note: When using an Anaconda distribution, the correct interpreter should have the suffix ('base':conda), for example Python 3.7.3 64-bit ('base':conda).

Selecting an interpreter sets the python.pythonPath value in your workspace settings to the path of the interpreter. To see the setting, select File > Preferences > Settings (Code > Preferences > Settings on macOS), then select the Workspace Settings tab.

Note: If you select an interpreter without a workspace folder open, VS Code sets python.pythonPath in your user settings instead, which sets the default interpreter for VS Code in general. The user setting makes sure you always have a default interpreter for Python projects. The workspace settings lets you override the user setting.

Create a Python Hello World source code file

From the File Explorer toolbar, select the New File button on the hello folder:

Name the file hello.py, and it automatically opens in the editor:

By using the .py file extension, you tell VS Code to interpret this file as a Python program, so that it evaluates the contents with the Python extension and the selected interpreter.

Note: The File Explorer toolbar also allows you to create folders within your workspace to better organize your code. You can use the New folder button to quickly create a folder.

Now that you have a code file in your Workspace, enter the following source code in hello.py:

When you start typing print, notice how IntelliSense presents auto-completion options.

IntelliSense and auto-completions work for standard Python modules as well as other packages you've installed into the environment of the selected Python interpreter. It also provides completions for methods available on object types. For example, because the msg variable contains a string, IntelliSense provides string methods when you type msg.:

Feel free to experiment with IntelliSense some more, but then revert your changes so you have only the msg variable and the print call, and save the file (⌘S (Windows, Linux Ctrl+S)).

For full details on editing, formatting, and refactoring, see Editing code. The Python extension also has full support for Linting.

Run Hello World

It's simple to run hello.py with Python. Just click the Run Python File in Terminal play button in the top-right side of the editor.

The button opens a terminal panel in which your Python interpreter is automatically activated, then runs python3 hello.py (macOS/Linux) or python hello.py (Windows):

There are three other ways you can run Python code within VS Code:

  • Right-click anywhere in the editor window and select Run Python File in Terminal (which saves the file automatically):

  • Select one or more lines, then press Shift+Enter or right-click and select Run Selection/Line in Python Terminal. This command is convenient for testing just a part of a file.

  • From the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), select the Python: Start REPL command to open a REPL terminal for the currently selected Python interpreter. In the REPL, you can then enter and run lines of code one at a time.

Configure and run the debugger

Let's now try debugging our simple Hello World program.

First, set a breakpoint on line 2 of hello.py by placing the cursor on the print call and pressing F9. Alternately, just click in the editor's left gutter, next to the line numbers. When you set a breakpoint, a red circle appears in the gutter.

Next, to initialize the debugger, press F5. Since this is your first time debugging this file, a configuration menu will open from the Command Palette allowing you to select the type of debug configuration you would like for the opened file.

Note: VS Code uses JSON files for all of its various configurations; launch.json is the standard name for a file containing debugging configurations.

These different configurations are fully explained in Debugging configurations; for now, just select Python File, which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.

The debugger will stop at the first line of the file breakpoint. The current line is indicated with a yellow arrow in the left margin. If you examine the Local variables window at this point, you will see now defined msg variable appears in the Local pane.

A debug toolbar appears along the top with the following commands from left to right: continue (F5), step over (F10), step into (F11), step out (⇧F11 (Windows, Linux Shift+F11)), restart (⇧⌘F5 (Windows, Linux Ctrl+Shift+F5)), and stop (⇧F5 (Windows, Linux Shift+F5)).

The Status Bar also changes color (orange in many themes) to indicate that you're in debug mode. The Python Debug Console also appears automatically in the lower right panel to show the commands being run, along with the program output.

To continue running the program, select the continue command on the debug toolbar (F5). The debugger runs the program to the end.

Tip Debugging information can also be seen by hovering over code, such as variables. In the case of msg, hovering over the variable will display the string Hello world in a box above the variable.

You can also work with variables in the Debug Console (If you don't see it, select Debug Console in the lower right area of VS Code, or select it from the ... menu.) Then try entering the following lines, one by one, at the > prompt at the bottom of the console:

Select the blue Continue button on the toolbar again (or press F5) to run the program to completion. 'Hello World' appears in the Python Debug Console if you switch back to it, and VS Code exits debugging mode once the program is complete.

If you restart the debugger, the debugger again stops on the first breakpoint.

To stop running a program before it's complete, use the red square stop button on the debug toolbar (⇧F5 (Windows, Linux Shift+F5)), or use the Run > Stop debugging menu command.

For full details, see Debugging configurations, which includes notes on how to use a specific Python interpreter for debugging.

Tip: Use Logpoints instead of print statements: Developers often litter source code with print statements to quickly inspect variables without necessarily stepping through each line of code in a debugger. In VS Code, you can instead use Logpoints. A Logpoint is like a breakpoint except that it logs a message to the console and doesn't stop the program. For more information, see Logpoints in the main VS Code debugging article.

Install and use packages

Let's now run an example that's a little more interesting. In Python, packages are how you obtain any number of useful code libraries, typically from PyPI. For this example, you use the matplotlib and numpy packages to create a graphical plot as is commonly done with data science. (Note that matplotlib cannot show graphs when running in the Windows Subsystem for Linux as it lacks the necessary UI support.)

Return to the Explorer view (the top-most icon on the left side, which shows files), create a new file called standardplot.py, and paste in the following source code:

Tip: If you enter the above code by hand, you may find that auto-completions change the names after the as keywords when you press Enter at the end of a line. To avoid this, type a space, then Enter.

Next, try running the file in the debugger using the 'Python: Current file' configuration as described in the last section.

Unless you're using an Anaconda distribution or have previously installed the matplotlib package, you should see the message, 'ModuleNotFoundError: No module named 'matplotlib'. Such a message indicates that the required package isn't available in your system.

Visual Studio Code Python Interpreter

To install the matplotlib package (which also installs numpy as a dependency), stop the debugger and use the Command Palette to run Terminal: Create New Integrated Terminal (⌃⇧` (Windows, Linux Ctrl+Shift+`)). This command opens a command prompt for your selected interpreter.

A best practice among Python developers is to avoid installing packages into a global interpreter environment. You instead use a project-specific virtual environment that contains a copy of a global interpreter. Once you activate that environment, any packages you then install are isolated from other environments. Such isolation reduces many complications that can arise from conflicting package versions. To create a virtual environment and install the required packages, enter the following commands as appropriate for your operating system:

Note: For additional information about virtual environments, see Environments.

  1. Create and activate the virtual environment

    Note: When you create a new virtual environment, you should be prompted by VS Code to set it as the default for your workspace folder. If selected, the environment will automatically be activated when you open a new terminal.

    For Windows

    If the activate command generates the message 'Activate.ps1 is not digitally signed. You cannot run this script on the current system.', then you need to temporarily change the PowerShell execution policy to allow scripts to run (see About Execution Policies in the PowerShell documentation):

    For macOS/Linux

  2. Select your new environment by using the Python: Select Interpreter command from the Command Palette.

  3. Install the packages

  4. Rerun the program now (with or without the debugger) and after a few moments a plot window appears with the output:

  5. Once you are finished, type deactivate in the terminal window to deactivate the virtual environment.

For additional examples of creating and activating a virtual environment and installing packages, see the Django tutorial and the Flask tutorial.

Next steps

You can configure VS Code to use any Python environment you have installed, including virtual and conda environments. You can also use a separate environment for debugging. For full details, see Environments.

To learn more about the Python language, follow any of the programming tutorials listed on python.org within the context of VS Code.

To learn to build web apps with the Django and Flask frameworks, see the following tutorials:

There is then much more to explore with Python in Visual Studio Code:

  • Editing code - Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
  • Linting - Enable, configure, and apply a variety of Python linters.
  • Debugging - Learn to debug Python both locally and remotely.
  • Testing - Configure test environments and discover, run, and debug tests.
  • Settings reference - Explore the full range of Python-related settings in VS Code.

An 'environment' in Python is the context in which a Python program runs. An environment consists of an interpreter and any number of installed packages. The Python extension for VS Code provides helpful integration features for working with different environments.

Note: If you're looking to get started with Python in Visual Studio Code, refer to the tutorial Getting Started with Python in VS Code.

Visual Studio Code Python Interpreter

Select and activate an environment

By default, the Python extension looks for and uses the first Python interpreter it finds in the system path. If it doesn't find an interpreter, it issues a warning. On macOS, the extension also issues a warning if you're using the OS-installed Python interpreter, because you typically want to use an interpreter you install directly. In either case, you can disable these warnings by setting python.disableInstallationCheck to true in your user settings.

To select a specific environment, use the Python: Select Interpreter command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).

You can switch environments at any time; switching environments helps you test different parts of your project with different interpreters or library versions as needed.

The Python: Select Interpreter command displays a list of available global environments, conda environments, and virtual environments. (See the Where the extension looks for environments section for details, including the distinctions between these types of environments.) The following image, for example, shows several Anaconda and CPython installations along with a conda environment and a virtual environment (env) that's located within the workspace folder:

Note: On Windows, it can take a little time for VS Code to detect available conda environments. During that process, you may see '(cached)' before the path to an environment. The label indicates that VS Code is presently working with cached information for that environment.

Selecting an interpreter from the list adds an entry for python.pythonPath with the path to the interpreter inside your Workspace Settings. Because the path is part of the workspace settings, the same environment should already be selected whenever you open that workspace. If you'd like to set up a default interpreter for your applications, you can instead add an entry for python.pythonPath manually inside your User Settings. To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and enter Preferences: Open User Settings. Then set python.pythonPath, which is in the Python extension section of User Settings, with the appropriate interpreter.

The Python extension uses the selected environment for running Python code (using the Python: Run Python File in Terminal command), providing language services (auto-complete, syntax checking, linting, formatting, etc.) when you have a .py file open in the editor, and opening a terminal with the Terminal: Create New Integrated Terminal command. In the latter case, VS Code automatically activated the selected environment.

Tip: To prevent automatic activation of a selected environment, add 'python.terminal.activateEnvironment': false to your settings.json file (it can be placed anywhere as a sibling to the existing settings).

Note: By default, VS Code uses the interpreter identified by python:pythonPath setting when debugging code. You can override this behavior by specifying a different path in the pythonPath property of a debug configuration. See Choose a debugging environment.

The Status Bar always shows the current interpreter.

The Status Bar also reflects when no interpreter is selected.

In either case, clicking this area of the Status Bar is a convenient shortcut for the Python: Select Interpreter command.

Tip: If you have any problems with VS Code recognizing a virtual environment, please file an issue in the documentation repository so we can help determine the cause.

Environments and Terminal windows

After using Python: Select Interpreter, that interpreter is applied when right-clicking a file and selecting Python: Run Python File in Terminal. The environment is also activated automatically when you use the Terminal: Create New Integrated Terminal command unless you change the python.terminal.activateEnvironment setting to false.

However, launching VS Code from a shell in which a certain Python environment is activated does not automatically activate that environment in the default Integrated Terminal. Use the Terminal: Create New Integrated Terminal command after VS Code is running.

Interpreter

Note: conda environments cannot be automatically activated in the integrated terminal if PowerShell is set as the integrated shell. See Integrated terminal - Configuration for how to change the shell.

Any changes you make to an activated environment within the terminal are persistent. For example, using conda install <package> from the terminal with a conda environment activated installs the package into that environment permanently. Similarly, using pip install in a terminal with a virtual environment activated adds the package to that environment.

Changing interpreters with the Python: Select Interpreter command doesn't affect terminal panels that are already open. You can thus activate separate environments in a split terminal: select the first interpreter, create a terminal for it, select a different interpreter, then use the split button ( (Windows, Linux Ctrl+Shift+5)) in the terminal title bar.

Choose a debugging environment

By default, the python.pythonPath setting specifies the Python interpreter to use for debugging. However, if you have a pythonPath property in the debug configuration of launch.json, that interpreter is used instead. To be more specific, VS Code applies the following order of precedence when determining which interpreter to use for debugging:

  1. pythonPath property of the selected debug configuration in launch.json
  2. python.pythonPath setting in the workspace settings.json
  3. python.pythonPath setting in the user settings.json

For more details on debug configuration, see Debugging configurations.

Where the extension looks for environments

Visual Studio Code Install Python Interpreter

The extension automatically looks for interpreters in the following locations:

  • Standard install paths such as /usr/local/bin, /usr/sbin, /sbin, c:python27, c:python36, etc.
  • Virtual environments located directly under the workspace (project) folder.
  • Virtual environments located in the folder identified by the python.venvPath setting (see General settings), which can contain multiple virtual environments. The extension looks for virtual environments in the first-level subfolders of venvPath.
  • Virtual environments located in a ~/.virtualenvs folder for virtualenvwrapper.
  • Interpreters installed by pyenv.
  • Virtual environments located in the path identified by WORKON_HOME (as used by virtualenvwrapper).
  • Conda environments that contain a Python interpreter. VS Code does not show conda environments that don't contain an interpreter.
  • Interpreters installed in a .direnv folder for direnv under the workspace (project) folder.

You can also manually specify an interpreter if Visual Studio Code does not locate it automatically.

Note: Once the 'select interpreter' flow is triggered, pipenv environments for the workspace folder will be searched for. If one is found, then no other interpreters are searched for or listed as pipenv expects to manage all aspects.

The extension also loads an environment variable definitions file identified by the python.envFile setting. The default value of this setting is ${workspaceFolder}/.env.

Global, virtual, and conda environments

By default, any Python interpreter that you've installed runs in its own global environment, which is not specific to any one project. For example, if you just run python (Windows) or python3 (macOS/Linux) at a new command prompt, you're running in that interpreter's global environment. Accordingly, any packages that you install or uninstall affect the global environment and all programs that you run within that context.

Note: The Python Extension version 2018.8.1 and later automatically updates environments.

Although working in the global environment is an easy way to get started, that environment will, over time, become cluttered with many different packages that you've installed for different projects. Such clutter makes it difficult to thoroughly test an application against a specific set of packages with known versions, which is exactly the kind of environment you'd set up on a build server or web server.

For this reason, developers often create a virtual environment for a project. A virtual environment is a subfolder in a project that contains a copy of a specific interpreter. When you activate the virtual environment, any packages you install are installed only in that environment's subfolder. When you then run a Python program within that environment, you know that it's running against only those specific packages. Be aware that if you're not using a virtual environment, and you have multiple versions of Python installed and set in the path variable, you might need to specify the Python interpreter to use in the terminal for installing packages to the global environment.

Note: While it's possible to open a virtual environment folder as a workspace, doing so is not recommended and might cause issues with using the Python extension.

Tip: A conda environment is a virtual environment that's created and managed using the conda package manager. See Conda environments for more details.

To create a virtual environment, use the following command, where '.venv' is the name of the environment folder:

Install

When you create a new virtual environment, a prompt will be displayed to allow you to select it for the workspace.

This will add the path to the Python interpreter from the new virtual environment to your workspace settings. That environment will then be used when installing packages and running code through the Python extension. For examples of using virtual environment in projects, see the Django tutorial and the Flask tutorial.

If the activate command generates the message 'Activate.ps1 is not digitally signed. You cannot run this script on the current system.', then you need to temporarily change the PowerShell execution policy to allow scripts to run (see About Execution Policies in the PowerShell documentation):

Note: If you're using a version of the Python extension prior to 2018.10, and you create a virtual environment in a VS Code terminal, you must run the Reload Window command from the Command Palette and then use Python: Select Interpreter to activate the environment. If you have any problems with VS Code recognizing a virtual environment, please file an issue in the documentation repository so we can help determine the cause.

Visual Studio Code Remote Python Interpreter

Tip: When you're ready to deploy the application to other computers, you can create a requirements.txt file with the command pip freeze > requirements.txt (pip3 on macOS/Linux). The requirements file describes the packages you've installed in your virtual environment. With only this file, you or other developers can restore those packages using pip install -r requirements.txt (or, again, pip3 on macOS/Linux). By using a requirements file, you need not commit the virtual environment itself to source control.

Conda environments

A conda environment is a Python environment that's managed using the conda package manager (see Getting started with conda (conda.io)). Conda works well to create environments with interrelated dependencies as well as binary packages. Unlike virtual environments, which are scoped to a project, conda environments are available globally on any given computer. This availability makes it easy to configure several distinct conda environments and then choose the appropriate one for any given project.

As noted earlier, the Python extension automatically detects existing conda environments provided that the environment contains a Python interpreter. For example, the following command creates a conda environment with the Python 3.4 interpreter and several libraries, which VS Code then shows in the list of available interpreters:

In contrast, if you fail to specify an interpreter, as with conda create --name env-00, the environment won't appear in the list.

For more information on the conda command line, see Conda environments (conda.io).

Additional notes:

  • If you create a new conda environment while VS Code is running, use the Reload Window command to refresh the environment list shown with Python: Select Interpreter; otherwise you may not see the environment there. It might take a short time to appear; if you don't see it at first, wait 15 seconds then try using the command again.

  • To ensure the environment is set up well from a shell perspective, one option is to use an Anaconda prompt with the activated environment to launch VS Code using the code . command. At that point you just need to select the interpreter using the Command Palette or by clicking on the status bar.

  • Although the Python extension for VS Code doesn't currently have direct integration with conda environment.yml files, VS Code itself is a great YAML editor.

  • Conda environments can't be automatically activated in the VS Code Integrated Terminal if the default shell is set to PowerShell. To change the shell, see Integrated terminal - Configuration.

  • You can manually specify the path to the conda executable to use for activation (version 4.4+). To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and enter Preferences: Open User Settings. Then set python.condaPath, which is in the Python extension section of User Settings, with the appropriate path.

Visual studio code python interpreter wsl

Manually specify an interpreter

If VS Code does not automatically locate an interpreter you want to use, you can set the path to it manually in your Workspace Settings settings.json file. With any of the entries that follow, you can just add the line as a sibling to other existing settings.)

First, select the File (Code on macOS) > Preferences > Settings menu command (⌘, (Windows, Linux Ctrl+,)) to open your Settings, select Workspace.

Then do any of the following steps:

  1. Create or modify an entry for python.pythonPath with the full path to the Python executable (if you edit settings.json directly, add the line below as the setting):

    For example:

    • Windows:

    • macOS/Linux:

  2. You can also use python.pythonPath to point to a virtual environment, for example:

    Windows:

    macOS/Linux:

  3. You can use an environment variable in the path setting using the syntax ${env:VARIABLE}. For example, if you've created a variable named PYTHON_INSTALL_LOC with a path to an interpreter, you can then use the following setting value:

    Note: Variable substitution is only supported in VS Code settings files, it will not work in .env environment files.

    By using an environment variable, you can easily transfer a project between operating systems where the paths are different, just be sure to set the environment variable on the operating system first.

Environment variable definitions file

An environment variable definitions file is a simple text file containing key-value pairs in the form of environment_variable=value, with # used for comments. Multiline values are not supported, but values can refer to any other environment variable that's already defined in the system or earlier in the file. For more information, see Variable substitution. Environment variable definitions files can be used for scenarios such as debugging and tool execution (including linters, formatters, IntelliSense, and testing tools), but are not applied to the terminal.

By default, the Python extension looks for and loads a file named .env in the current workspace folder, then applies those definitions. The file is identified by the default entry 'python.envFile': '${workspaceFolder}/.env' in your user settings (see General settings). You can change the python.envFile setting at any time to use a different definitions file.

A debug configuration also contains an envFile property that also defaults to the .env file in the current workspace (see Debugging - Set configuration options). This property allows you to easily set variables for debugging purposes that replace variables specified in the default .env file.

For example, when developing a web application, you might want to easily switch between development and production servers. Instead of coding the different URLs and other settings into your application directly, you could use separate definitions files for each. For example:

dev.env file

prod.env file

You can then set the python.envFile setting to ${workspaceFolder}/prod.env, then set the envFile property in the debug configuration to ${workspaceFolder}/dev.env.

Note: When environment variables are specified using multiple methods, be aware that there is an order of precedence. Environment variables contained in the .env file specified by the python.envFile setting (user or workspace) will override variables defined in the envFile specified in launch.json, as well as any env variables defined in the launch.json file itself. Similarly, environment variables defined in the envFile specified in launch.json will override env variables defined in the launch.json file.

Variable substitution

When defining an environment variable in a definitions file, you can use the value of any existing environment variable with the following general syntax:

where ... means any other text as used in the value. The curly braces are required.

Visual Studio Code Add Python Interpreter

Within this syntax, the following rules apply:

  • Variables are processed in the order they appear in the .env file, so you can use any variable that's defined earlier in the file.
  • Single or double quotes don't affect substituted value and are included in the defined value. For example, if the value of VAR1 is abcedfg, then VAR2='${env:VAR1}' assigns the value 'abcedfg' to VAR2.
  • The $ character can be escaped with a backslash, as in $.
  • You can use recursive substitution, such as PYTHONPATH=${env:PROJ_DIR}:${env:PYTHONPATH} (where PROJ_DIR is any other environment variable).
  • You can use only simple substitution; nesting such as ${_${env:VAR1}_EX} is not supported.
  • Entries with unsupported syntax are left as-is.

Use of the PYTHONPATH variable

The PYTHONPATH environment variable specifies additional locations where the Python interpreter should look for modules. In VS Code, PYTHONPATH can be set through the terminal settings (terminal.integrated.env.*) and/or within an .env file.

When the terminal settings are used, PYTHONPATH affects any tools that are run within the terminal by a user, as well as any action the extension performs for a user that is routed through the terminal such as debugging. However, in this case when the extension is performing an action that isn't routed through the terminal, such as the use of a linter or formatter, then this setting will not have an effect on module look-up.

Visual Studio Code Python Virtual Environment

When PYTHONPATH is set using an .env file, it will affect anything the extension does on your behalf and actions performed by the debugger, but it will not affect tools run in the terminal.

If needed, you can set PYTHONPATH using both methods.

An example of when to use PYTHONPATH would be if you have source code in a src folder and tests in a tests folder. When running tests, however, those tests can't normally access modules in src unless you hard-code relative paths. To solve this problem, add the path to src to PYTHONPATH.

The value of PYTHONPATH can contain multiple locations separated by os.pathsep: a semicolon (;) on Windows and a colon (:) on Linux/macOS. Invalid paths are ignored. If you find that your value for PYTHONPATH isn't working as expected, make sure that you're using the correct separator between locations for the operating system. For example, using a colon to separate locations on Windows, or using a semicolon to separate locations on Linux/macOS results in an invalid value for PYTHONPATH, which is ignored.

Note: PYTHONPATH does not specify a path to a Python interpreter itself, and should not be used with the python.pythonPath setting. For additional information about PYTHONPATH, read the PYTHONPATH documentation.

Vs Code Python Virtualenv

Next steps

Visual Studio Code Python Interpreter Path

  • Editing code - Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
  • Debugging - Learn to debug Python both locally and remotely.
  • Testing - Configure test environments and discover, run, and debug tests.
  • Settings reference - Explore the full range of Python-related settings in VS Code.